






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class        UpperCamelCase__ (				a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = KandinskyInpaintPipeline
    __UpperCAmelCase           = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
    __UpperCAmelCase           = [
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
        """mask_image""",
    ]
    __UpperCAmelCase           = [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    __UpperCAmelCase           = False







    @property
    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           return 3_2







    @property
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           return 3_2







    @property
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           return self.time_input_dim







    @property
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           return self.time_input_dim * 4







    @property
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           return 1_0_0







    @property
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
           return tokenizer







    @property
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           torch.manual_seed(0 )
           __magic_name__      				=    MCLIPConfig(
               numDims=self.cross_attention_dim				,    transformerDimensions=self.text_embedder_hidden_size				,    hidden_size=self.text_embedder_hidden_size				,    intermediate_size=3_7				,    num_attention_heads=4				,    num_hidden_layers=5				,    vocab_size=1_0_0_5				,    )

           __magic_name__      				=    MultilingualCLIP(UpperCamelCase_ )
           __magic_name__      				=    text_encoder.eval()

           return text_encoder







    @property
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           torch.manual_seed(0 )

           __magic_name__      				=    {
               'in_channels': 9,
               # Out channels is double in channels because predicts mean and variance
               'out_channels': 8,
               'addition_embed_type': 'text_image',
               'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
               'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
               'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
               'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
               'layers_per_block': 1,
               'encoder_hid_dim': self.text_embedder_hidden_size,
               'encoder_hid_dim_type': 'text_image_proj',
               'cross_attention_dim': self.cross_attention_dim,
               'attention_head_dim': 4,
               'resnet_time_scale_shift': 'scale_shift',
               'class_embed_type': None,
           }

           __magic_name__      				=    UNetaDConditionModel(**UpperCamelCase_ )
           return model







    @property
    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           return {
               "block_out_channels": [3_2, 6_4],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 1_2,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }







    @property
    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           torch.manual_seed(0 )
           __magic_name__      				=    VQModel(**self.dummy_movq_kwargs )
           return model







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.dummy_text_encoder
           __magic_name__      				=    self.dummy_tokenizer
           __magic_name__      				=    self.dummy_unet
           __magic_name__      				=    self.dummy_movq

           __magic_name__      				=    DDIMScheduler(
               num_train_timesteps=1_0_0_0				,    beta_schedule='linear'				,    beta_start=0.00_085				,    beta_end=0.012				,    clip_sample=UpperCamelCase_				,    set_alpha_to_one=UpperCamelCase_				,    steps_offset=1				,    prediction_type='epsilon'				,    thresholding=UpperCamelCase_				,    )

           __magic_name__      				=    {
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components







    def 				a__			(		self :	Optional[int]				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	str=0 ):





           '''simple docstring'''

           __magic_name__      				=    floats_tensor((1, self.cross_attention_dim)				,    rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           __magic_name__      				=    floats_tensor((1, self.cross_attention_dim)				,    rng=random.Random(seed + 1 ) ).to(UpperCamelCase_ )
           # create init_image
           __magic_name__      				=    floats_tensor((1, 3, 6_4, 6_4)				,    rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           __magic_name__      				=    image.cpu().permute(0				,    2				,    3				,    1 )[0]
           __magic_name__      				=    Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('RGB' ).resize((2_5_6, 2_5_6) )
           # create mask
           __magic_name__      				=    np.ones((6_4, 6_4)				,    dtype=np.floataa )
           __magic_name__      				=    0

           if str(UpperCamelCase_ ).startswith('mps' ):
                  __magic_name__      				=    torch.manual_seed(UpperCamelCase_ )
           else:
                  __magic_name__      				=    torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
           __magic_name__      				=    {
               'prompt': 'horse',
               'image': init_image,
               'mask_image': mask,
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'generator': generator,
               'height': 6_4,
               'width': 6_4,
               'num_inference_steps': 2,
               'guidance_scale': 4.0,
               'output_type': 'np',
           }
           return inputs







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    'cpu'

           __magic_name__      				=    self.get_dummy_components()

           __magic_name__      				=    self.pipeline_class(**UpperCamelCase_ )
           __magic_name__      				=    pipe.to(UpperCamelCase_ )

           pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
           __magic_name__      				=    output.images

           __magic_name__      				=    pipe(
               **self.get_dummy_inputs(UpperCamelCase_ )				,    return_dict=UpperCamelCase_				,    )[0]

           __magic_name__      				=    image[0, -3:, -3:, -1]
           __magic_name__      				=    image_from_tuple[0, -3:, -3:, -1]

           print(f"""image.shape {image.shape}""" )

           assert image.shape == (1, 6_4, 6_4, 3)

           __magic_name__      				=    np.array(
               [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786] )

           assert (
               np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
           ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
           ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           super().test_inference_batch_single_identical(expected_max_diff=3e-3 )



@slow
@require_torch_gpu
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy' )

           __magic_name__      				=    load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
           __magic_name__      				=    np.ones((7_6_8, 7_6_8)				,    dtype=np.floataa )
           __magic_name__      				=    0

           __magic_name__      				=    'a hat'

           __magic_name__      				=    KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior'				,    torch_dtype=torch.floataa )
           pipe_prior.to(UpperCamelCase_ )

           __magic_name__      				=    KandinskyInpaintPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-inpaint'				,    torch_dtype=torch.floataa )
           __magic_name__      				=    pipeline.to(UpperCamelCase_ )
           pipeline.set_progress_bar_config(disable=UpperCamelCase_ )

           __magic_name__      				=    torch.Generator(device='cpu' ).manual_seed(0 )
           __magic_name__				,  __magic_name__      				=    pipe_prior(
               UpperCamelCase_				,    generator=UpperCamelCase_				,    num_inference_steps=5				,    negative_prompt=''				,    ).to_tuple()

           __magic_name__      				=    pipeline(
               UpperCamelCase_				,    image=UpperCamelCase_				,    mask_image=UpperCamelCase_				,    image_embeds=UpperCamelCase_				,    negative_image_embeds=UpperCamelCase_				,    generator=UpperCamelCase_				,    num_inference_steps=1_0_0				,    height=7_6_8				,    width=7_6_8				,    output_type='np'				,    )

           __magic_name__      				=    output.images[0]

           assert image.shape == (7_6_8, 7_6_8, 3)

           assert_mean_pixel_difference(UpperCamelCase_				,    UpperCamelCase_ )







"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							A						(				__snake_case: Tuple  )    ->		Optional[Any]:

       """simple docstring"""





       if (
           (cp >= 0x4_E00 and cp <= 0x9_FFF)
           or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
           or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
           or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
           or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
           or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
           or (cp >= 0xF_900 and cp <= 0xF_AFF)
           or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
       ):  #
              return True

       return False


def 							A						(				__snake_case: str  )    ->		Optional[Any]:

       """simple docstring"""





       for char in word:
              __magic_name__      				=    ord(__snake_case  )
              if not _is_chinese_char(__snake_case  ):
                     return 0
       return 1


def 							A						(				__snake_case: List[str]  )    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    set()

       for token in tokens:
              __magic_name__      				=    len(__snake_case  ) > 1 and is_chinese(__snake_case  )
              if chinese_word:
                     word_set.add(__snake_case  )
       __magic_name__      				=    list(__snake_case  )
       return word_list


def 							A						(				__snake_case: List[str] ,     __snake_case: set()  )    ->		str:

       """simple docstring"""





       if not chinese_word_set:
              return bert_tokens
       __magic_name__      				=    max([len(__snake_case  ) for w in chinese_word_set]  )

       __magic_name__      				=    bert_tokens
       __magic_name__				,  __magic_name__      				=    0, len(__snake_case  )
       while start < end:
              __magic_name__      				=    True
              if is_chinese(bert_word[start]  ):
                     __magic_name__      				=    min(end - start ,     __snake_case  )
                     for i in range(__snake_case ,     1 ,     -1  ):
                            __magic_name__      				=    ''.join(bert_word[start : start + i]  )
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,     start + i  ):
                                          __magic_name__      				=    '##' + bert_word[j]
                                   __magic_name__      				=    start + i
                                   __magic_name__      				=    False
                                   break
              if single_word:
                     start += 1
       return bert_word


def 							A						(				__snake_case: List[str] ,     __snake_case: LTP ,     __snake_case: BertTokenizer  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    []

       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,     tasks=['cws']  ).cws
              __magic_name__      				=    [get_chinese_word(__snake_case  ) for r in res]
              ltp_res.extend(__snake_case  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    bert_tokenizer(lines[i : i + 1_0_0] ,     add_special_tokens=__snake_case ,     truncation=__snake_case ,     max_length=5_1_2  )
              bert_res.extend(res['input_ids']  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for input_ids, chinese_word in zip(__snake_case ,     __snake_case  ):
              __magic_name__      				=    []
              for id in input_ids:
                     __magic_name__      				=    bert_tokenizer._convert_id_to_token(__snake_case  )
                     input_tokens.append(__snake_case  )
              __magic_name__      				=    add_sub_symbol(__snake_case ,     __snake_case  )
              __magic_name__      				=    []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__snake_case  ):
                     if token[:2] == "##":
                            __magic_name__      				=    token[2:]
                            # save chinese tokens' pos
                            if len(__snake_case  ) == 1 and _is_chinese_char(ord(__snake_case  )  ):
                                   ref_id.append(__snake_case  )
              ref_ids.append(__snake_case  )

       assert len(__snake_case  ) == len(__snake_case  )

       return ref_ids


def 							A						(				__snake_case: Optional[Any]  )    ->		List[str]:

       """simple docstring"""





       with open(args.file_name ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    f.readlines()
       __magic_name__      				=    [line.strip() for line in data if len(__snake_case  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       __magic_name__      				=    LTP(args.ltp  )  # faster in GPU device
       __magic_name__      				=    BertTokenizer.from_pretrained(args.bert  )

       __magic_name__      				=    prepare_ref(__snake_case ,     __snake_case ,     __snake_case  )

       with open(args.save_path ,     'w' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.dumps(__snake_case  ) + '\n' for ref in ref_ids]
              f.writelines(__snake_case  )


if __name__ == "__main__":
 snake_case : Tuple     		=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     required=False,
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""",
     required=False,
     type=str,
     default="""./resources/ltp""",
     help="""resources for LTP tokenizer, usually a path""",
 )
 parser.add_argument(
     """--bert""",
     required=False,
     type=str,
     default="""./resources/robert""",
     help="""resources for Bert tokenizer""",
 )
 parser.add_argument(
     """--save_path""",
     required=False,
     type=str,
     default="""./resources/ref.txt""",
     help="""path to save res""",
 )

 snake_case : Any     		=		parser.parse_args()
 main(args)